import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import os

files=os.listdir()[-8:]
files

['uber-raw-data-apr14.csv',
 'uber-raw-data-aug14.csv',
 'uber-raw-data-janjune-15.csv',
 'uber-raw-data-jul14.csv',
 'uber-raw-data-jun14.csv',
 'uber-raw-data-may14.csv',
 'uber-raw-data-sep14.csv',
 'Uber.ipynb']

files.remove('uber-raw-data-janjune-15.csv')
files.remove('Uber.ipynb')

files

['uber-raw-data-apr14.csv',
 'uber-raw-data-aug14.csv',
 'uber-raw-data-jul14.csv',
 'uber-raw-data-jun14.csv',
 'uber-raw-data-may14.csv',
 'uber-raw-data-sep14.csv']

#blank dataframe
final=pd.DataFrame()

for file in files:
    df=pd.read_csv(file,encoding='utf-8')
    final=pd.concat([df,final])

final.shape

(4534327, 4)

final.head()

	Date/Time	Lat	Lon	Base
0	9/1/2014 0:01:00	40.2201	-74.0021	B02512
1	9/1/2014 0:01:00	40.7500	-74.0027	B02512
2	9/1/2014 0:03:00	40.7559	-73.9864	B02512
3	9/1/2014 0:06:00	40.7450	-73.9889	B02512
4	9/1/2014 0:11:00	40.8145	-73.9444	B02512

df = final.copy()

df.dtypes

Date/Time     object
Lat          float64
Lon          float64
Base          object
dtype: object

df['Date/Time'] = pd.to_datetime(df['Date/Time'],format="%m/%d/%Y %H:%M:%S")

df.dtypes

Date/Time    datetime64[ns]
Lat                 float64
Lon                 float64
Base                 object
dtype: object

df['weekday']=df['Date/Time'].dt.day_name()
df['day']=df['Date/Time'].dt.day
df['minute']=df['Date/Time'].dt.minute
df['month']=df['Date/Time'].dt.month
df['hour']=df['Date/Time'].dt.hour

df.head()

	Date/Time	Lat	Lon	Base	weekday	day	minute	month	hour
0	2014-09-01 00:01:00	40.2201	-74.0021	B02512	Monday	1	1	9	0
1	2014-09-01 00:01:00	40.7500	-74.0027	B02512	Monday	1	1	9	0
2	2014-09-01 00:03:00	40.7559	-73.9864	B02512	Monday	1	3	9	0
3	2014-09-01 00:06:00	40.7450	-73.9889	B02512	Monday	1	6	9	0
4	2014-09-01 00:11:00	40.8145	-73.9444	B02512	Monday	1	11	9	0

df['Base'].unique()

array(['B02512', 'B02598', 'B02617', 'B02682', 'B02764'], dtype=object)

df['day'].unique()

array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31],
      dtype=int64)

df['weekday'].unique()

array(['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday',
       'Sunday'], dtype=object)

pip install plotly

Requirement already satisfied: plotly in c:\programdata\anaconda3\lib\site-packages (4.14.3)
Requirement already satisfied: retrying>=1.3.3 in c:\programdata\anaconda3\lib\site-packages (from plotly) (1.3.3)
Requirement already satisfied: six in c:\programdata\anaconda3\lib\site-packages (from plotly) (1.15.0)
Note: you may need to restart the kernel to use updated packages.

import plotly.express as px

px.bar(x=df['weekday'].value_counts().index,
      y=df['weekday'].value_counts().values
      )

for i,month in enumerate(df['month'].unique()):
    print(month)

plt.figure(figsize=(60,30))
for i,month in enumerate(df['month'].unique()):
    plt.subplot(3,2,i+1)
    df[df['month']==month]['hour'].hist()

9
5
6
7
8
4

## Analysis of Rush of each hour in each month
for i in df['month'].unique():
    plt.figure(figsize=(5,3))
    plt.title("Month {}".format(i)  )
    df[df['month']==i]['hour'].hist()
    

# conda install -c plotly chart-studio

Collecting package metadata (current_repodata.json): ...working... done
Solving environment: ...working... done

# All requested packages already installed.


Note: you may need to restart the kernel to use updated packages.

import chart_studio.plotly as py
import plotly.graph_objs as go
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot

#Choose Max Month For Riding 
trace1 = go.Bar( 
        x = df.groupby('month')['hour'].sum().index,
        y = df.groupby('month')['hour'].sum(),
        name= 'Priority')
iplot([trace1])

# Analysis for Journey for Each  Day
plt.figure(figsize=(10,6))
plt.hist(df['day'], bins=30, rwidth=.8, range=(0.5, 30.5))
plt.xlabel('date of the month')
plt.ylabel('Total Journeys')
plt.title('Journeys by Month Day');

df['month'].unique()

array([9, 5, 6, 7, 8, 4], dtype=int64)

##  Analysis of Total rides month-wise
plt.figure(figsize=(20,10))
for i,month in enumerate(df['month'].unique(),1):
    plt.subplot(3,2,i)
    df_out=df[df['month']==month]
    plt.hist(df_out['day'])
    plt.xlabel('days in month {}'.format(month))
    plt.ylabel('total rides')

sns.set_style(style='whitegrid')
ax = sns.pointplot(x="hour",y="Lat",data=df,hue='weekday')
ax.set_title("hours of day Vs latitude of passenger ")

Text(0.5, 1.0, 'hours of day Vs latitude of passenger ')

df.groupby(['Base','month'])['Date/Time'].count()

Base    month
B02512  4         35536
        5         36765
        6         32509
        7         35021
        8         31472
        9         34370
B02598  4        183263
        5        260549
        6        242975
        7        245597
        8        220129
        9        240600
B02617  4        108001
        5        122734
        6        184460
        7        310160
        8        355803
        9        377695
B02682  4        227808
        5        222883
        6        194926
        7        196754
        8        173280
        9        197138
B02764  4          9908
        5          9504
        6          8974
        7          8589
        8         48591
        9        178333
Name: Date/Time, dtype: int64

base=df.groupby(['Base','month'])['Date/Time'].count().reset_index()
base

	Base	month	Date/Time
0	B02512	4	35536
1	B02512	5	36765
2	B02512	6	32509
3	B02512	7	35021
4	B02512	8	31472
5	B02512	9	34370
6	B02598	4	183263
7	B02598	5	260549
8	B02598	6	242975
9	B02598	7	245597
10	B02598	8	220129
11	B02598	9	240600
12	B02617	4	108001
13	B02617	5	122734
14	B02617	6	184460
15	B02617	7	310160
16	B02617	8	355803
17	B02617	9	377695
18	B02682	4	227808
19	B02682	5	222883
20	B02682	6	194926
21	B02682	7	196754
22	B02682	8	173280
23	B02682	9	197138
24	B02764	4	9908
25	B02764	5	9504
26	B02764	6	8974
27	B02764	7	8589
28	B02764	8	48591
29	B02764	9	178333

## which base number gets popular by month name 
plt.figure(figsize=(10,6))
sns.lineplot(x='month',y='Date/Time',hue='Base',data=base);

def count_rows(rows):
    return len(rows)

by_cross = df.groupby(['weekday','hour']).apply(count_rows)
by_cross

weekday    hour
Friday     0       13716
           1        8163
           2        5350
           3        6930
           4        8806
                   ...  
Wednesday  19      47017
           20      47772
           21      44553
           22      32868
           23      18146
Length: 168, dtype: int64

pivot=by_cross.unstack()
pivot

hour	0	1	2	3	4	5	6	7	8	9	...	14	15	16	17	18	19	20	21	22	23
weekday
Friday	13716	8163	5350	6930	8806	13450	23412	32061	31509	25230	...	36206	43673	48169	51961	54762	49595	43542	48323	49409	41260
Monday	6436	3737	2938	6232	9640	15032	23746	31159	29265	22197	...	28157	32744	38770	42023	37000	34159	32849	28925	20158	11811
Saturday	27633	19189	12710	9542	6846	7084	8579	11014	14411	17669	...	31418	38769	43512	42844	45883	41098	38714	43826	47951	43174
Sunday	32877	23015	15436	10597	6374	6169	6596	8728	12128	16401	...	28151	31112	33038	31521	28291	25948	25076	23967	19566	12166
Thursday	9293	5290	3719	5637	8505	14169	27065	37038	35431	27812	...	36699	44442	50560	56704	55825	51907	51990	51953	44194	27764
Tuesday	6237	3509	2571	4494	7548	14241	26872	36599	33934	25023	...	34846	41338	48667	55500	50186	44789	44661	39913	27712	14869
Wednesday	7644	4324	3141	4855	7511	13794	26943	36495	33826	25635	...	35148	43388	50684	55637	52732	47017	47772	44553	32868	18146

7 rows × 24 columns

plt.figure(figsize=(10,6))
sns.heatmap(pivot, annot=False);

def heatmap(col1,col2):
    by_cross = df.groupby([col1,col2]).apply(lambda x:len(x))
    pivot=by_cross.unstack()
    plt.figure(figsize=(10,6))
    return sns.heatmap(pivot,annot=False)

heatmap('day','hour');

heatmap('day','month');

We observe that the number of trips increases each month, we can say that from April to September 2014, Uber was in a continuous improvement process.

##  Analysis of Location data points
plt.figure(figsize=(10,6))

plt.plot(df['Lon'], df['Lat'],'r+', ms=0.5)
plt.xlim(-74.2, -73.7)
plt.ylim(40.6,41);

df_out=df[df['weekday']=='Sunday']
df_out.head()

	Date/Time	Lat	Lon	Base	weekday	day	minute	month	hour
8011	2014-09-07 00:00:00	40.7341	-74.0005	B02512	Sunday	7	0	9	0
8012	2014-09-07 00:00:00	40.7344	-73.9900	B02512	Sunday	7	0	9	0
8013	2014-09-07 00:00:00	40.7806	-73.9582	B02512	Sunday	7	0	9	0
8014	2014-09-07 00:01:00	40.7293	-73.9859	B02512	Sunday	7	1	9	0
8015	2014-09-07 00:01:00	40.7713	-74.0133	B02512	Sunday	7	1	9	0

df_out.groupby(['Lat','Lon'])['weekday'].count().reset_index()

	Lat	Lon	weekday
0	39.9374	-74.0722	1
1	39.9378	-74.0721	1
2	39.9384	-74.0742	1
3	39.9385	-74.0734	1
4	39.9415	-74.0736	1
...	...	...	...
209225	41.3141	-74.1249	1
209226	41.3180	-74.1298	1
209227	41.3195	-73.6905	1
209228	41.3197	-73.6903	1
209229	42.1166	-72.0666	1

209230 rows × 3 columns

import folium
from folium.plugins import HeatMap
basemap=folium.Map()

HeatMap(df_out.groupby(['Lat','Lon'])['weekday'].count().reset_index(),zoom=20,radius=15).add_to(basemap)
basemap

Make this Notebook Trusted to load map: File -> Trust Notebook

##function for a specific day
def plot(df,day):
    df_out=df[df['weekday']==day]
    df_out.groupby(['Lat','Lon'])['weekday'].count().reset_index()
    HeatMap(df_out.groupby(['Lat','Lon'])['weekday'].count().reset_index(),zoom=20,radius=15).add_to(basemap)
    return basemap

plot(df,'Sunday')

Make this Notebook Trusted to load map: File -> Trust Notebook

Analysis of Jan-June uber_15

uber_15 = pd.read_csv('uber-raw-data-janjune-15.csv',encoding='utf-8')
uber_15.head()

	Dispatching_base_num	Pickup_date	Affiliated_base_num	locationID
0	B02617	2015-05-17 09:47:00	B02617	141
1	B02617	2015-05-17 09:47:00	B02617	65
2	B02617	2015-05-17 09:47:00	B02617	100
3	B02617	2015-05-17 09:47:00	B02774	80
4	B02617	2015-05-17 09:47:00	B02617	90

uber_15.dtypes

Dispatching_base_num    object
Pickup_date             object
Affiliated_base_num     object
locationID               int64
dtype: object

uber_15['Pickup_date'] =  pd.to_datetime(uber_15['Pickup_date'], format='%Y-%m-%d %H:%M:%S')

uber_15['weekday']=uber_15['Pickup_date'].dt.day_name()
uber_15['day']=uber_15['Pickup_date'].dt.day
uber_15['minute']=uber_15['Pickup_date'].dt.minute
uber_15['month']=uber_15['Pickup_date'].dt.month
uber_15['hour']=uber_15['Pickup_date'].dt.hour

uber_15.head()

	Dispatching_base_num	Pickup_date	Affiliated_base_num	locationID	weekday	day	minute	month	hour
0	B02617	2015-05-17 09:47:00	B02617	141	Sunday	17	47	5	9
1	B02617	2015-05-17 09:47:00	B02617	65	Sunday	17	47	5	9
2	B02617	2015-05-17 09:47:00	B02617	100	Sunday	17	47	5	9
3	B02617	2015-05-17 09:47:00	B02774	80	Sunday	17	47	5	9
4	B02617	2015-05-17 09:47:00	B02617	90	Sunday	17	47	5	9

## Uber pickups by the month in New york City
px.bar(x=uber_15['month'].value_counts().index,
           y=uber_15['month'].value_counts().values)

## Analysing Rush in New york City
plt.figure(figsize=(12,6))
sns.countplot(uber_15['hour']);

C:\ProgramData\Anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

## Analysing In-Depth Analysis of Rush in New york City Day & hour wise 
summary=uber_15.groupby(['weekday', 'hour'])['Pickup_date'].count().reset_index()
summary=summary.rename(columns = {'Pickup_date':'Counts'})
summary

	weekday	hour	Counts
0	Friday	0	85939
1	Friday	1	46616
2	Friday	2	28102
3	Friday	3	19518
4	Friday	4	23575
...	...	...	...
163	Wednesday	19	143751
164	Wednesday	20	136003
165	Wednesday	21	133993
166	Wednesday	22	127026
167	Wednesday	23	99490

168 rows × 3 columns

plt.figure(figsize=(10,6))
sns.pointplot(x="hour", y="Counts", hue="weekday", data=summary);

##### Loading Uber-Jan-Feb-FOIL.csv
uber_foil=pd.read_csv('Uber-Jan-Feb-FOIL.csv')

uber_foil.head()

	dispatching_base_number	date	active_vehicles	trips
0	B02512	1/1/2015	190	1132
1	B02765	1/1/2015	225	1765
2	B02764	1/1/2015	3427	29421
3	B02682	1/1/2015	945	7679
4	B02617	1/1/2015	1228	9537

uber_foil['dispatching_base_number'].unique()

array(['B02512', 'B02765', 'B02764', 'B02682', 'B02617', 'B02598'],
      dtype=object)

plt.figure(figsize=(12,6))
sns.boxplot(x = 'dispatching_base_number', y = 'active_vehicles', data = uber_foil);

seems to have more number of trips in B02764

# Finding the ratio of trips/active_vehicles
uber_foil['trips/vehicle'] = uber_foil['trips']/uber_foil['active_vehicles']

uber_foil.head()

	dispatching_base_number	date	active_vehicles	trips	trips/vehicle
0	B02512	1/1/2015	190	1132	5.957895
1	B02765	1/1/2015	225	1765	7.844444
2	B02764	1/1/2015	3427	29421	8.585060
3	B02682	1/1/2015	945	7679	8.125926
4	B02617	1/1/2015	1228	9537	7.766287

uber_foil.set_index('date')

	dispatching_base_number	active_vehicles	trips	trips/vehicle
date
1/1/2015	B02512	190	1132	5.957895
1/1/2015	B02765	225	1765	7.844444
1/1/2015	B02764	3427	29421	8.585060
1/1/2015	B02682	945	7679	8.125926
1/1/2015	B02617	1228	9537	7.766287
...	...	...	...	...
2/28/2015	B02764	3952	39812	10.073887
2/28/2015	B02617	1372	14022	10.220117
2/28/2015	B02682	1386	14472	10.441558
2/28/2015	B02512	230	1803	7.839130
2/28/2015	B02765	747	7753	10.378849

354 rows × 4 columns

## how Average trips/vehicle inc/decreases with dates with each of base umber
plt.figure(figsize=(10,6))
uber_foil.set_index('date').groupby(['dispatching_base_number'])['trips/vehicle'].plot()
plt.ylabel('Average trips/vehicle')
plt.title('Demand vs Supply chart (Date-wise)')
plt.legend()

C:\ProgramData\Anaconda3\lib\site-packages\pandas\plotting\_matplotlib\core.py:1192: UserWarning:

FixedFormatter should only be used together with FixedLocator

<matplotlib.legend.Legend at 0x20dad48ebe0>